TxGemma：用于治疗学的高效智能体大型语言模型
TxGemma: Efficient and Agentic LLMs for Therapeutics
AI总结：
本文提出TxGemma系列通用治疗学LLM及Agentic-Tx智能体系统，在66项治疗开发任务中多数超越现有通用和专用模型，并显著提升化学与生物学基准测试成绩。
摘要：
治疗药物开发是一项成本高昂且高风险的工程，常常受到高失败率的困扰。为解决这一问题，我们提出了TxGemma，这是一套高效、通用的LLM，既能进行治疗性质预测，也能进行交互式推理和可解释性分析。与任务专用模型不同，TxGemma能够综合来自不同来源的信息，从而在治疗药物开发全流程中实现广泛应用。该套件包含2B、9B和27B参数规模的模型，基于Gemma-2在小分子、蛋白质、核酸、疾病和细胞系的综合数据集上进行微调。在66项治疗药物开发任务中，TxGemma在64项任务上达到或超越了最先进的通用模型性能（其中45项表现更优），并在50项任务上对比最先进的专用模型时表现更优或相当（其中26项表现更优）。在治疗下游任务（如临床试验不良事件预测）上微调TxGemma模型所需的训练数据少于微调基础LLM，这使得TxGemma适用于数据有限的应用场景。除了这些预测能力之外，TxGemma还具备对话式模型，弥合了通用LLM与专用性质预测器之间的差距。这些模型使科学家能够以自然语言进行交互，基于分子结构为预测提供机制性推理，并参与科学讨论。在此基础上，我们进一步提出了Agentic-Tx，这是一个由Gemini 2.5驱动的通用治疗智能体系统，能够推理、行动、管理多样化工作流程并获取外部领域知识。Agentic-Tx在Humanity's Last Exam基准测试（化学与生物学）上超越了此前的领先模型，相对o3-mini (high)提升了52.3%，在GPQA（化学）上相对o3-mini (high)提升了26.7%，并在ChemBench-Preference和ChemBench-Mini上分别以6.3%和2.4%的提升优于o3-mini (high)。
英文摘要：
Therapeutic development is a costly and high-risk endeavor that is often plagued by high failure rates. To address this, we introduce TxGemma, a suite of efficient, generalist large language models (LLMs) capable of therapeutic property prediction as well as interactive reasoning and explainability. Unlike task-specific models, TxGemma synthesizes information from diverse sources, enabling broad application across the therapeutic development pipeline. The suite includes 2B, 9B, and 27B parameter models, fine-tuned from Gemma-2 on a comprehensive dataset of small molecules, proteins, nucleic acids, diseases, and cell lines. Across 66 therapeutic development tasks, TxGemma achieved superior or comparable performance to the state-of-the-art generalist model on 64 (superior on 45), and against state-of-the-art specialist models on 50 (superior on 26). Fine-tuning TxGemma models on therapeutic downstream tasks, such as clinical trial adverse event prediction, requires less training data than fine-tuning base LLMs, making TxGemma suitable for data-limited applications. Beyond these predictive capabilities, TxGemma features conversational models that bridge the gap between general LLMs and specialized property predictors. These allow scientists to interact in natural language, provide mechanistic reasoning for predictions based on molecular structure, and engage in scientific discussions. Building on this, we further introduce Agentic-Tx, a generalist therapeutic agentic system powered by Gemini 2.5 that reasons, acts, manages diverse workflows, and acquires external domain knowledge. Agentic-Tx surpasses prior leading models on the Humanity's Last Exam benchmark (Chemistry & Biology) with 52.3% relative improvement over o3-mini (high) and 26.7% over o3-mini (high) on GPQA (Chemistry) and excels with improvements of 6.3% (ChemBench-Preference) and 2.4% (ChemBench-Mini) over o3-mini (high).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
